Managing Microplastics in Saint John, New Brunswick: A Grassroots Action Utilizing the One Health Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plastic pollution in marine environments has become a global crisis, with microplastics posing significant threats to the health of all one health model stakeholders: humans, non-human animals, and ecosystems. The persistent and pervasive nature of plastics makes ocean plastic pollution a complex and interconnected “wicked problem.” This paper explores the LINT LUV-R initiative by the Atlantic Coastal Action Program in Saint John, a grassroots, community-based approach to preventing microplastic and microfiber accumulation in Saint John Harbour by installing microfiber filters in washing machines. Unique in its preventive methodology, this initiative captures microfibers before they enter aquatic ecosystems, addressing a critical source of microplastic contamination in this region. Grounded in the principles of One Health, this initiative recognizes the interdependence of human, non-human animal, and environmental health. This inclusive approach fosters collaboration among diverse stakeholders, including Indigenous communities, fishers, environmental nonprofits, government agencies, and the community, to promote sustainable solutions for Saint John Harbour. The initiative demonstrates measurable success, capturing millions of microfibers annually while empowering participants through education and citizen science. By combining preventive action, cultural sensitivity, and stakeholder engagement, the LINT LUV-R initiative offers a replicable model for combating microplastic pollution in other coastal regions. This paper highlights the necessity of community-led, multidisciplinary approaches to solve wicked environmental problems and advance the health of interconnected systems, prioritizing the health of non-human animals, humans, and ecosystems equally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it